Point cloud analysis is challenging due to irregularity and unordered data structure. To capture the 3D geometries, prior works mainly rely on exploring sophisticated local geometric extractors using convolution, graph, or attention mechanisms. These methods, however, incur unfavorable latency during inference, and the performance saturates over the past few years. In this paper, we present a novel perspective on this task. We notice that detailed local geometrical information probably is not the key to point cloud analysis -- we introduce a pure residual MLP network, called PointMLP, which integrates no sophisticated local geometrical extractors but still performs very competitively. Equipped with a proposed lightweight geometric affine module, PointMLP delivers the new state-of-the-art on multiple datasets. On the real-world ScanObjectNN dataset, our method even surpasses the prior best method by 3.3% accuracy. We emphasize that PointMLP achieves this strong performance without any sophisticated operations, hence leading to a superior inference speed. Compared to most recent CurveNet, PointMLP trains 2x faster, tests 7x faster, and is more accurate on ModelNet40 benchmark. We hope our PointMLP may help the community towards a better understanding of point cloud analysis. The code is available at https://github.com/ma-xu/pointMLP-pytorch.

本文提出了一种名为PointMLP的纯残差MLP网络，它不使用复杂的局部几何提取器，但仍能表现出很高的竞争力，装备了提出的轻量级几何仿射模块，PointMLP在多个数据集上都达到了新的最佳状态，相对于大多数最近的CurveNet，PointMLP的训练速度快两倍，测试速度快七倍，并在ModelNet40基准测试中获得更高的精度。

在点云中重新思考网络设计和本地几何：一个简单的残差MLP框架